Non-invasive imaging is one of the major factors that propelled medical science and treatment over the past decades. By assessing the characteristics of human tissue, imaging is often used in clinical practice for oncologic diagnosis and treatment guidance. A key goal of imaging is personalized medicine, were the treatment is tailored towards the specific characteristics of the patient. For personalized medicine, recent developments in the fields of genomics and proteomics have enabled the molecular characterization of tissue, such as neoplasms (i.e. tumors). However, as tumors are spatially and temporally heterogeneous, these techniques are limited, as they require biopsies or invasive surgeries to extract part of the tissue under study. Therefore, imaging can be of large importance as imaging can capture intra-tumoral heterogeneity, is already performed in routine practice, is non-invasive, and can be acquired during time.
Probably the most widespread imaging modality is computed tomography (CT), assessing the density of the tissue. Indeed, CT images of lung cancer exhibit strong differences in tumor intensity, texture, and shape. However, in clinical practice only measures describing tumor size are routinely extracted to assess response to therapy. While tumor size has demonstrated its clinical validity, it does not reflect pathological differences. There are investigations that have identified this appearance of the tumor on CT images, to provide qualitatively information about tumor characteristics like type, degree, spread, or aggressiveness. However, these characteristics are typically described subjectively (e.g. “moderate heterogeneity”, “highly spiculated”, “large necrotic core”). Moreover, recent advances in image acquisition, standardization, and image analysis, now allows for objective imaging biomarkers that are potentially prognostic or predictive.
Despite the above advancements, interpretation of imaging data of tumors remains to be a complex task. As a result, there is a need for sophisticated data analysis methods that enable to derive information from imaging data which aids oncologists and medical practitioners in selecting a proper curative treatment.